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Selecting Lung Cancer Patients from UK Primary Care Data: A Longitudinal Study of Feature Trends

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Abstract

A high proportion of lung cancer cases are detected at a late cancer stage when they present with symptoms to general practitioners (GP). Early diagnosis is a challenge because many symptoms are also common in other diseases. Therefore, this study aims to assess UK primary care data of patients one, two and three years prior to lung cancer diagnosis to capture trends in clinical features of patients with the goal of early diagnosis and thus potentially curative treatment. This longitudinal study utilises data from the Clinical Practice Research Datalink (CPRD) with linked data from the National Cancer Registration and Analysis Service (NCRAS). A comprehensive list of Read codes is created to select features of interest to establish if a patient has experienced a certain medical condition or not. The comparison of the relative frequencies of the identified predictors associated with cases and controls reveals the importance of the following groups of features: ‘Cough Wheeze’ and ‘Bronchitis unspecified’, ‘Dyspnoea’ and ‘Upper Respiratory Infection’, which are frequent events for lung cancer cases, where a high proportion of cases were also identified using ‘Haemoptysis’ and ‘Peripheral vascular disease’.

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Acknowledgement

We would like to thank the Medical Technologies and Advanced Materials Strategic Research Theme at Nottingham Trent University for financial support.

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Authors and Affiliations

  1. Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK

    Abeer Alzubaidi, Mufti Mahmud, David J. Brown & Jun He

  2. Department of Respiratory Medicine, Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, NG5 1PB, UK

    David R. Baldwin & Emma O’Dowd

  3. Computing and Informatics Research Centre, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK

    Mufti Mahmud & David J. Brown

  4. Medical Technologies Innovation Facility, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK

    Mufti Mahmud & David J. Brown

  5. School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK

    Graham Ball

  6. Division of Epidemiology and Public Health, University of Nottingham, Nottingham, NG5 1PB, UK

    Jaspreet Kaur, David R. Baldwin, Emma O’Dowd & Richard B. Hubbard

Authors
  1. Abeer Alzubaidi

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  2. Jaspreet Kaur

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  3. Mufti Mahmud

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  4. David J. Brown

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  5. Jun He

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  6. Graham Ball

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  7. David R. Baldwin

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  8. Emma O’Dowd

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  9. Richard B. Hubbard

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Corresponding author

Correspondence toMufti Mahmud.

Editor information

Editors and Affiliations

  1. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  2. Jahangirnagar University, Savar, Dhaka, Bangladesh

    M. Shamim Kaiser

  3. Auckland University of Technology, Auckland, New Zealand

    Nikola Kasabov

  4. Old Dominion University, Norfolk, VA, USA

    Khan Iftekharuddin

  5. Maebashi Institute of Technology, Maebashi, Japan

    Ning Zhong

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Alzubaidi, A.et al. (2021). Selecting Lung Cancer Patients from UK Primary Care Data: A Longitudinal Study of Feature Trends. In: Mahmud, M., Kaiser, M.S., Kasabov, N., Iftekharuddin, K., Zhong, N. (eds) Applied Intelligence and Informatics. AII 2021. Communications in Computer and Information Science, vol 1435. Springer, Cham. https://doi.org/10.1007/978-3-030-82269-9_4

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